Contour Dice Loss for Structures with Fuzzy and Complex Boundaries in Fetal MRI

Bella Specktor-Fadida; Bossmat Yehuda; Daphna Link-Sourani; Liat Ben-Sira; Dafna Ben-Bashat; Leo Joskowicz

doi:10.1007/978-3-031-25066-8_19

Contour Dice Loss for Structures with Fuzzy and Complex Boundaries in Fetal MRI

Bella Specktor-Fadida^*, Bossmat Yehuda, Daphna Link-Sourani, Liat Ben-Sira, Dafna Ben-Bashat, Leo Joskowicz

^*Corresponding author for this work

The Rachel and Selim Benin School of Engineering and Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Volumetric measurements of fetal structures in MRI are time consuming and error prone and therefore require automatic segmentation. Placenta segmentation and accurate fetal brain segmentation for gyrification assessment are particularly challenging because of the placenta fuzzy boundaries and the fetal brain cortex complex foldings. In this paper, we study the use of the Contour Dice loss for both problems and compare it to other boundary losses and to the combined Dice and Cross-Entropy loss. The loss is computed efficiently for each slice via erosion, dilation and XOR operators. We describe a new formulation of the loss akin to the Contour Dice metric. The combination of the Dice loss and the Contour Dice yielded the best performance for placenta segmentation. For fetal brain segmentation, the best performing loss was the combined Dice with Cross-Entropy loss followed by the Dice with Contour Dice loss, which performed better than other boundary losses.

Original language	English
Title of host publication	Computer Vision – ECCV 2022 Workshops, Proceedings
Editors	Leonid Karlinsky, Tomer Michaeli, Ko Nishino
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	355-368
Number of pages	14
ISBN (Print)	9783031250651
DOIs	https://doi.org/10.1007/978-3-031-25066-8_19
State	Published - 2023
Event	17th European Conference on Computer Vision, ECCV 2022 - Tel Aviv, Israel Duration: 23 Oct 2022 → 27 Oct 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13803 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	17th European Conference on Computer Vision, ECCV 2022
Country/Territory	Israel
City	Tel Aviv
Period	23/10/22 → 27/10/22

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

Deep learning segmentation
Fetal MRI
Segmentation contour

Access to Document

10.1007/978-3-031-25066-8_19

Cite this

Specktor-Fadida, B., Yehuda, B., Link-Sourani, D., Ben-Sira, L., Ben-Bashat, D., & Joskowicz, L. (2023). Contour Dice Loss for Structures with Fuzzy and Complex Boundaries in Fetal MRI. In L. Karlinsky, T. Michaeli, & K. Nishino (Eds.), Computer Vision – ECCV 2022 Workshops, Proceedings (pp. 355-368). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13803 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-25066-8_19

Specktor-Fadida, Bella ; Yehuda, Bossmat ; Link-Sourani, Daphna et al. / Contour Dice Loss for Structures with Fuzzy and Complex Boundaries in Fetal MRI. Computer Vision – ECCV 2022 Workshops, Proceedings. editor / Leonid Karlinsky ; Tomer Michaeli ; Ko Nishino. Springer Science and Business Media Deutschland GmbH, 2023. pp. 355-368 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Volumetric measurements of fetal structures in MRI are time consuming and error prone and therefore require automatic segmentation. Placenta segmentation and accurate fetal brain segmentation for gyrification assessment are particularly challenging because of the placenta fuzzy boundaries and the fetal brain cortex complex foldings. In this paper, we study the use of the Contour Dice loss for both problems and compare it to other boundary losses and to the combined Dice and Cross-Entropy loss. The loss is computed efficiently for each slice via erosion, dilation and XOR operators. We describe a new formulation of the loss akin to the Contour Dice metric. The combination of the Dice loss and the Contour Dice yielded the best performance for placenta segmentation. For fetal brain segmentation, the best performing loss was the combined Dice with Cross-Entropy loss followed by the Dice with Contour Dice loss, which performed better than other boundary losses.",

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Specktor-Fadida, B, Yehuda, B, Link-Sourani, D, Ben-Sira, L, Ben-Bashat, D & Joskowicz, L 2023, Contour Dice Loss for Structures with Fuzzy and Complex Boundaries in Fetal MRI. in L Karlinsky, T Michaeli & K Nishino (eds), Computer Vision – ECCV 2022 Workshops, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13803 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 355-368, 17th European Conference on Computer Vision, ECCV 2022, Tel Aviv, Israel, 23/10/22. https://doi.org/10.1007/978-3-031-25066-8_19

Contour Dice Loss for Structures with Fuzzy and Complex Boundaries in Fetal MRI. / Specktor-Fadida, Bella; Yehuda, Bossmat; Link-Sourani, Daphna et al.
Computer Vision – ECCV 2022 Workshops, Proceedings. ed. / Leonid Karlinsky; Tomer Michaeli; Ko Nishino. Springer Science and Business Media Deutschland GmbH, 2023. p. 355-368 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13803 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Specktor-Fadida B, Yehuda B, Link-Sourani D, Ben-Sira L, Ben-Bashat D, Joskowicz L. Contour Dice Loss for Structures with Fuzzy and Complex Boundaries in Fetal MRI. In Karlinsky L, Michaeli T, Nishino K, editors, Computer Vision – ECCV 2022 Workshops, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 355-368. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-25066-8_19